Others propose an end-to-end process, whereby image to control decisions
are learnt instead [1]}, [2]}.
In the cases where expert demonstration is employed, it is not clear that
the expert's motives for the control decisions necessarily translate to
desirable navigation choices given the mobile robot's goals. When the
control objective is clear, as in stabilization, then the image to control
mapping is effectively injective, thereby reducing mismatch between implicit
information contained in the training set and the explicit goal
[3]}. Another issue that arises with many end-to-end methods is that the
underlying strategy is functionally a regressor [4]}.
For navigation, which is non-convex due to the topological splits that occur
in trajectory space, regression may not be the best choice.
Similar problems may arise for methods that learn motion primitives for
specific scenarios [5]}, as a particular motion
primitive may be biased towards one topological instance of a reaction
from a set of potential reactions. The bias may undermine the global
objective.
